Basic Overview

Visualize the Learning Process

Last Stable Release

pip install clairvoyant

Latest Development Changes

git clone https://github.com/anfederico/Clairvoyant

Backtesting Signal Accuracy

During the testing period, the model signals to buy or sell based on its prediction for price
movement the following day. By putting your trading algorithm aside and testing for signal accuracy
alone, you can rapidly build and test more reliable models.

Simulate a Trading Strategy

Once you've established your model can accurately predict price movement a day in advance,
simulate a portfolio and test your performance with a particular stock. User defined trading logic
lets you control the flow of your capital based on the model's confidence in its prediction
and the following next day outcome.

Other Projects

Intensive Backtesting

The primary purpose of this project is to rapidly test datasets on machine learning algorithms (specifically Support Vector Machines). While the Simulation class allows for basic strategy testing, there are other projects more suited for that task. Once you've tested patterns within your data and simulated a basic strategy, I'd recommend taking your model to the next level with:

https://github.com/anfederico/Gemini

Social Sentiment Scores

The examples shown use data derived from a project where we are data mining social media and performing stock sentiment analysis. To get an idea of how we do that, please take a look at: